Glucose level management based on fat content of meals

ABSTRACT

Techniques for glucose level management are disclosed. In some examples, the techniques may involve obtaining an estimated protein value for a meal and determining an equivalent carbohydrate value for the meal based on the estimated protein value. In some examples, the techniques may involve obtaining an estimated fat value for a meal and determining, based on the estimated fat value, a glucose absorption rate resulting from consumption of the meal. In some examples, the equivalent carbohydrate value and/or the glucose absorption rate may be used to determine an amount of insulin to deliver to a patient to counteract a glucose level increase caused by consumption of the meal. The techniques may further involve generating an output indicative of the amount of insulin to deliver to the patient to counteract the glucose level increase caused by consumption of the meal.

TECHNICAL FIELD

The disclosure relates to medical systems and, more particularly, tomedical systems for therapy for diabetes.

BACKGROUND

A patient with diabetes typically receives insulin from an insulindelivery device (e.g., a pump or injection device) to control theglucose level in his or her bloodstream. Naturally produced insulin maynot control the glucose level in the bloodstream of a diabetes patientdue to insufficient production of insulin and/or due to insulinresistance. To control the glucose level, a patient's therapy routinemay include basal dosages and bolus dosages of insulin. Basal dosagestend to keep glucose levels at consistent levels during periods offasting. Bolus dosages may be delivered to the patient specifically ator near mealtimes or other times where there may be a relatively fastchange in glucose level.

SUMMARY

Disclosed herein are techniques for glucose level management. Thetechniques may be practiced using systems; processor-implementedmethods; and non-transitory processor-readable storage media storinginstructions which, when executed by one or more processors, causeperformance of the techniques.

In some embodiments, the techniques may involve obtaining a fat valuefor a meal. The techniques may further involve determining, based on theestimated fat value, a glucose absorption rate resulting fromconsumption of the meal. The techniques may also involve determining,based on the glucose absorption rate resulting from consumption of themeal, an amount of insulin to deliver to a patient to counteract aglucose level increase caused by consumption of the meal. Additionally,the techniques may involve generating an output indicative of the amountof insulin to deliver to the patient to counteract the glucose levelincrease caused by consumption of the meal.

The details of one or more aspects of the disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of this disclosure will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example glucose levelmanagement system comprising an insulin pump, in accordance with one ormore examples described in this disclosure.

FIG. 2 is a block diagram illustrating an example glucose levelmanagement system comprising a manual injection device, in accordancewith one or more examples described in this disclosure.

FIG. 3 is a block diagram illustrating an example glucose levelmanagement system comprising a networked injection device, in accordancewith one or more examples described in this disclosure.

FIG. 4 is a block diagram illustrating an example of a patient device,in accordance with one or more examples described in this disclosure.

FIGS. 5A and 5B are example plots of glucose level over time afterconsuming a meal.

FIGS. 6-8 are flow diagrams illustrating example techniques for glucoselevel management, in accordance with the techniques of this disclosure.

DETAILED DESCRIPTION

Disclosed herein are devices, systems, and techniques for managing aglucose level of a patient based on information about one or moremacronutrients (e.g., protein and/or fat) other than carbohydrates. Apatient's glucose level after consuming a meal is heavily influenced bythe carbohydrate content of the meal. In general, for meals with highercarbohydrate contents, higher glucose levels are expected for thepatient after consuming the meal. However, managing glucose levels basedonly on the carbohydrate content of the meal fails to account for othermacronutrients that impact glucose levels. For example, the proteincontent and/or the fat content of the meal can affect the glucose levelof the patient after consuming the meal.

To account for other macronutrients in the meal, a physiological modelof the patient may be configured to take, as input, information aboutone or more macronutrients other than carbohydrates. For example, acomputing device of this disclosure may be configured to create anequivalent carbohydrate content for the meal based on the proteincontent of the meal. One gram of protein may not have the same effect onglucose levels as one gram of carbohydrates, so the computing device mayuse a conversion factor to scale a value of the protein content.Additionally or alternatively, the fat content of the meal may affectthe rate at which the patient's body absorbs glucose after consuming themeal. Thus, meals differing in fat content may have different peakglucose levels after the meal and/or different timings of the peakglucose levels after the meal. Accordingly, the computing device may usea tuning factor to approximate the effect of the fat content on glucoselevels. More specifically, the tuning factor may be used toapproximately the effect of the fat content on reducing the rate atwhich the patient's body absorbs glucose. In some examples, a tuningfactor with a large magnitude limits the effect of fat on reducing theabsorption rate more so than a tuning factor with a small magnitude. Ingraphical terms, the glucose profile (e.g., plot of glucose levels overtime) corresponding to a fatty meal may appear to be a flattened curve(e.g., having a peak glucose level that is lower and that occurs laterin time) relative to a glucose profile corresponding to a meal withoutfat.

By accounting for the protein content and/or the fat content of a meal,a computing device implementing the physiological model may predictpostprandial glucose levels with greater accuracy. Additionally oralternatively, the computing device may determine a meal bolus dosagethat will maximize time-in-range.

It should be appreciated that the techniques disclosed herein can bepracticed with one or more types of insulin (e.g., fast-acting insulin,intermediate-acting insulin, and/or slow-acting insulin). Thus, termssuch as “basal insulin” and “bolus insulin” do not necessarily denotedifferent types of insulin. For example, fast-acting insulin may be usedfor both basal dosages and bolus dosages.

FIG. 1 is a block diagram illustrating an example glucose levelmanagement system, in accordance with one or more examples described inthis disclosure. FIG. 1 illustrates system 10A that includes insulinpump 14, tubing 16, infusion set 18, monitoring device 20 (e.g., aglucose level monitoring device), wearable device 22, patient device 24,and cloud 26. Cloud 26 represents a local, wide area or global computingnetwork including one or more processors 28A-28N (“one or moreprocessors 28”). In some examples, the various components may determinechanges to therapy based on determination of glucose level formonitoring device 20, and therefore system 10A may be referred to asglucose level management system 10A.

Patient 12 may be diabetic (e.g., Type 1 diabetic or Type 2 diabetic),and therefore, the glucose level in patient 12 may be controlled withdelivery of supplemental insulin. For example, patient 12 may notproduce sufficient insulin to control the glucose level or the amount ofinsulin that patient 12 produces may not be sufficient due to insulinresistance that patient 12 may have developed.

To receive the supplemental insulin, patient 12 may carry insulin pump14 that couples to tubing 16 for delivery of insulin into patient 12.Infusion set 18 may connect to the skin of patient 12 and include acannula to deliver insulin into patient 12. Monitoring device 20 mayalso be coupled to patient 12 to measure glucose level in patient 12.Insulin pump 14, tubing 16, infusion set 18, and monitoring device 20may together form an insulin pump system. One example of the insulinpump system is the MINIMED™ 670G insulin pump system by MEDTRONICMINIMED, INC. However, other examples of insulin pump systems may beused and the example techniques should not be considered limited to theMINIMED™ 670G insulin pump system. For example, the techniques describedin this disclosure may be utilized in insulin pump systems that includewireless communication capabilities. However, the example techniquesshould not be considered limited to insulin pump systems with wirelesscommunication capabilities, and other types of communication, such aswired communication, may be possible. In another example, insulin pump14, tubing 16, infusion set 18, and/or monitoring device 20 may becontained in the same housing.

Insulin pump 14 may be a relatively small device that patient 12 canplace in different locations. For instance, patient 12 may clip insulinpump 14 to the waistband of pants worn by patient 12. In some examples,to be discreet, patient 12 may place insulin pump 14 in a pocket. Ingeneral, insulin pump 14 can be worn in various places, and patient 12may place insulin pump 14 in a location based on the particular clothespatient 12 is wearing. Other insulin delivery devices (e.g., injectiondevices, inhaler devices, etc.) may be used in addition to or as analternative to insulin pump 14.

To deliver insulin, insulin pump 14 includes one or more reservoirs(e.g., two reservoirs). A reservoir may be a plastic cartridge thatholds up to N units of insulin (e.g., up to 300 units of insulin) and islocked into insulin pump 14. Insulin pump 14 may be a battery-powereddevice that is powered by replaceable and/or rechargeable batteries.

Tubing 16 may connect at a first end to a reservoir in insulin pump 14and may connect at a second end to infusion set 18. Tubing 16 may carrythe insulin from the reservoir of insulin pump 14 to patient 12. Tubing16 may be flexible, allowing for looping or bends to minimize concern oftubing 16 becoming detached from insulin pump 14 or infusion set 18 orconcern from tubing 16 breaking.

Infusion set 18 may include a thin cannula that patient 12 inserts intoa layer of fat under the skin (e.g., subcutaneous connection). Infusionset 18 may rest near the stomach of patient 12. The insulin may travelfrom the reservoir of insulin pump 14, through tubing 16, through thecannula in infusion set 18, and into patient 12. In some examples,patient 12 may utilize an infusion set insertion device. Patient 12 mayplace infusion set 18 into the infusion set insertion device, and with apush of a button on the infusion set insertion device, the infusion setinsertion device may insert the cannula of infusion set 18 into thelayer of fat of patient 12, and infusion set 18 may rest on top of theskin of the patient with the cannula inserted into the layer of fat ofpatient 12.

Monitoring device 20 may include a sensor that is inserted under theskin of patient 12, such as near the stomach of patient 12 or in the armof patient 12 (e.g., subcutaneous connection). Monitoring device 20 maybe configured to measure the interstitial glucose level, which is theglucose found in the fluid between the cells of patient 12. Monitoringdevice 20 may be configured to continuously or periodically sample theglucose level and rate of change of the glucose level over time.

In one or more examples, insulin pump 14, monitoring device 20, and/orthe various components illustrated in FIG. 1, may together form aclosed-loop therapy delivery system. For example, patient 12 may set atarget glucose level, usually measured in units of milligrams perdeciliter, on insulin pump 14. Insulin pump 14 may receive the currentglucose level from monitoring device 20 and, in response, may increaseor decrease the amount of insulin delivered to patient 12. For example,if the current glucose level is higher than the target glucose level,insulin pump 14 may increase the insulin. If the current glucose levelis lower than the target glucose level, insulin pump 14 may temporarilycease delivery of the insulin. Insulin pump 14 may be considered as anexample of an automated insulin delivery (AID) device. Other examples ofAID devices may be possible, and the techniques described in thisdisclosure may be applicable to other AID devices.

Insulin pump 14 and monitoring device 20 may be configured to operatetogether to mimic some of the ways in which a healthy pancreas works.Insulin pump 14 may be configured to deliver basal dosages, which aresmall amounts of insulin released continuously throughout the day. Theremay be times when glucose levels increase, such as due to eating or someother activity that patient 12 undertakes. Insulin pump 14 may beconfigured to deliver bolus dosages on demand in association with foodintake or to correct an undesirably high glucose level in thebloodstream. In one or more examples, if the glucose level rises above atarget level, then insulin pump 14 may deliver a bolus dosage to addressthe increase in glucose level. Insulin pump 14 may be configured tocompute basal and bolus dosages and deliver the basal and bolus dosagesaccordingly. For instance, insulin pump 14 may determine the amount of abasal dosage to deliver continuously and then determine the amount of abolus dosage to deliver to reduce glucose level in response to anincrease in glucose level due to eating or some other event.

Accordingly, in some examples, monitoring device 20 may sample glucoselevels for determining rate of change in glucose level over time.Monitoring device 20 may output the glucose level to insulin pump 14(e.g., through a wireless link connection like Bluetooth or BLE).Insulin pump 14 may compare the glucose level to a target glucose level(e.g., as set by patient 12 or a clinician) and adjust the insulindosage based on the comparison.

As described above, patient 12 or a clinician may set one or more targetglucose levels on insulin pump 14. There may be various ways in whichpatient 12 or the clinician may set a target glucose level on insulinpump 14. As one example, patient 12 or the clinician may utilize patientdevice 24 to communicate with insulin pump 14. Examples of patientdevice 24 include mobile devices, such as smartphones, tablet computers,laptop computers, and the like. In some examples, patient device 24 maybe a special programmer or controller (e.g., a dedicated remote controldevice) for insulin pump 14. Although FIG. 1 illustrates one patientdevice 24, in some examples, there may be a plurality of patientdevices. For instance, system 10A may include a mobile device and adedicated wireless controller, each of which is an example of patientdevice 24. For ease of description only, the example techniques aredescribed with respect to patient device 24 with the understanding thatpatient device 24 may be one or more patient devices.

Patient device 24 may also be configured to interface with monitoringdevice 20. As one example, patient device 24 may receive informationfrom monitoring device 20 through insulin pump 14, where insulin pump 14relays the information between patient device 24 and monitoring device20. As another example, patient device 24 may receive information (e.g.,glucose level or rate of change of glucose level) directly frommonitoring device 20 (e.g., through a wireless link). Patient device 24may include processing circuitry configured to receive user inputindicative of macronutrient values for a meal that patient 12 hasconsumed, is consuming, or will consume.

In one or more examples, patient device 24 may comprise a user interfacewith which patient 12 or the clinician may control insulin pump 14. Forexample, patient device 24 may comprise a touchscreen that allowspatient 12 or the clinician to enter a target glucose level.Additionally or alternatively, patient device 24 may comprise a displaydevice that outputs the current and/or past glucose level. In someexamples, patient device 24 may output notifications to patient 12, suchas notifications if the glucose level is too high or too low, as well asnotifications regarding any action that patient 12 needs to take. Forexample, if the batteries of insulin pump 14 are low on charge, theninsulin pump 14 may output a low battery indication to patient device24, and patient device 24 may in turn output a notification to patient12 to replace or recharge the batteries.

Controlling insulin pump 14 through a display device of patient device24 is merely provided as an example and should not be consideredlimiting. For example, insulin pump 14 may include pushbuttons thatallow patient 12 or the clinician to set the various glucose levels ofinsulin pump 14. In some examples, insulin pump 14 itself, or inaddition to patient device 24, may be configured to output notificationsto patient 12. For instance, if the glucose level is too high or toolow, insulin pump 14 may output an audible or haptic output. In someexamples, if the battery is low, then insulin pump 14 may output a lowbattery indication on a display of insulin pump 14.

As mentioned above, insulin pump 14 may deliver insulin to patient 12based on current glucose levels (e.g., as measured by monitoring device20). However, it should be appreciated that insulin delivery is notlimited to implementations based on current glucose levels. For example,insulin pump 14 may deliver insulin to patient 12 based on a predictedglucose level (e.g., a future glucose level that is determined based ona glucose level trend).

The glucose level of patient 12 may be affected by patient 12 engagingin an activity like eating or exercising. There may be therapeuticbenefits to delivering/not delivering insulin based on determining thatpatient 12 is engaging in the activity.

As illustrated in FIG. 1, patient 12 may wear wearable device 22.Examples of wearable device 22 include a smartwatch or a fitnesstracker, either of which may, in some examples, be configured to be wornon a patient's wrist or arm. In one or more examples, wearable device 22includes an inertial measurement unit, such as a six-axis inertialmeasurement unit. The inertial measurement unit may include anaccelerometer (e.g., a 3-axis accelerometer) and a gyroscope (e.g., a3-axis gyroscope). Accelerometers measure linear acceleration, whilegyroscopes measure rotational motion. Wearable device 22 may beconfigured to determine one or more movement characteristics of patient12. Examples of the one or more movement characteristics include valuesrelating to frequency, amplitude, trajectory, position, velocity,acceleration and/or pattern of movement instantaneously or over time.The frequency of movement of the patient's arm may refer to how manytimes patient 12 repeated a movement within a certain time (e.g., suchas frequency of movement back and forth between two positions).

Patient 12 may wear wearable device 22 on his or her wrist. However, theexample techniques are not so limited. For example, patient 12 may wearwearable device 22 on a finger, forearm, or bicep. In general, patient12 may wear wearable device 22 anywhere that can be used to determinegestures indicative of eating.

The manner in which patient 12 is moving his or her arm (i.e., themovement characteristics) may refer to the direction, angle, andorientation of the movement of the arm of patient 12, including valuesrelating to frequency, amplitude, trajectory, position, velocity,acceleration and/or pattern of movement instantaneously or over time. Asan example, if patient 12 is eating, then the arm of patient 12 will beoriented in a particular way (e.g., thumb is facing towards the body ofpatient 12), the angle of movement of the arm will be approximately a90-degree movement (e.g., starting from plate to mouth), and thedirection of movement of the arm will be a path that follows from plateto mouth. The forward/backward, up/down, pitch, roll, yaw measurementsfrom wearable device 22 may be indicative of the manner in which patient12 is moving his or her arm. Also, patient 12 may have a certainfrequency with which patient 12 moves his or her arm or a pattern withwhich patient 12 moves his or her arm that is more indicative of eating,as compared to other activities, like smoking or vaping, where patient12 may raise his or her arm to his or her mouth.

Although the above description describes wearable device 22 as beingutilized to determine whether patient 12 is eating, wearable device 22may be configured to detect any activity undertaken by patient 12. Forinstance, the movement characteristics detected by wearable device 22may indicate whether patient 12 is exercising, driving, sleeping, etc.In some examples, wearable device 22 may detect a posture of patient 12that corresponds with a posture for exercising, driving, sleeping,eating, etc. Another term for movement characteristics may be gesturemovements. Accordingly, wearable device 22 may be configured to detectgesture movements (i.e., movement characteristics of the arm of patient12) and/or posture, where the gesture and/or posture may be part ofvarious activities (e.g., eating, exercising, driving, sleeping, etc.).

In some examples, wearable device 22 may be configured to determine,based on the detected gestures (e.g., movement characteristics of thearm of patient 12) and/or posture, the particular activity patient 12 isundertaking. For example, wearable device 22 may be configured todetermine whether patient 12 is eating, exercising, driving, sleeping,etc. In some examples, wearable device 22 may output informationindicative of the movement characteristics of the arm of patient 12and/or posture of patient 12 to one or more other devices (e.g., patientdevice 24 and/or one or more server computers in cloud 26), and the oneor more other devices may be configured to determine the activitypatient 12 is undertaking.

As illustrated in FIG. 1, system 10A includes cloud 26 that includes oneor more processors 28. For example, cloud 26 may include a plurality ofnetwork devices (e.g., servers), and each network device may include oneor more processors. One or more processors 28 may be distributed acrossthe plurality of network devices or may be located within a single oneof the network devices. Cloud 26 represents a computing infrastructurethat supports one or more processors 28 which may execute applicationsor operations requested by one or more users. For example, one or moreprocessors 28 may remotely store, manage, and/or process data that wouldotherwise be locally stored, managed, and/or processed by patient device24 or wearable device 22. One or more processors 28 may share data orresources for performing computations and may be part of computingservers, web servers, database servers, and the like. One or moreprocessors 28 may be in network devices (e.g., servers) within adatacenter or may be distributed across multiple datacenters. In somecases, the datacenters may be in different geographical locations.

One or more processors 28, as well as other processing circuitrydescribed herein, can include one or more of any of the following:microprocessors, digital signal processors (DSPs), application specificintegrated circuits (ASICs), field programmable gate arrays (FPGAs), orany other equivalent integrated or discrete logic circuitry, as well asany combinations of such components. The functions attributed to one ormore processors 28, as well as other processing circuitry describedherein may be embodied as hardware, firmware, software, or anycombination thereof.

One or more processors 28 may be implemented as fixed-function circuits,programmable circuits, or a combination thereof. Fixed-function circuitsrefer to circuits that provide particular functionality and are preseton the operations that can be performed. Programmable circuits refer tocircuits that can be programmed to perform various tasks and provideflexible functionality in the operations that can be performed. Forinstance, programmable circuits may execute software or firmware thatcause the programmable circuits to operate in the manner defined byinstructions of the software or firmware. Fixed-function circuits mayexecute software instructions (e.g., to receive parameters or outputparameters), but the types of operations that the fixed-functioncircuits perform are generally immutable. In some examples, one or moreprocessors 28 may include distinct circuit blocks (fixed-function orprogrammable), and in some examples, one or more processors 28 mayinclude integrated circuits. One or more processors 28 may includearithmetic logic units (ALUs), elementary function units (EFUs), digitalcircuits, analog circuits, and/or programmable cores, formed fromprogrammable circuits. In examples where the operations of one or moreprocessors 28 are performed using software executed by the programmablecircuits, memory (e.g., on servers in cloud 26) accessible by one ormore processors 28 may store the object code of the software that one ormore processors 28 receive and execute.

In some examples, one or more processors 28 may be configured todetermine patterns from gesture movements (e.g., one or more movementcharacteristics determined by wearable device 22) and configured todetermine a particular activity patient 12 is undertaking. One or moreprocessors 28 may provide responsive real-time cloud services that can,on a responsive real-time basis, determine the activity patient 12 isundertaking, and in some examples, provide recommended therapy (e.g.,insulin dosage amount). Cloud 26 and patient device 24 may communicatevia one or more networks (e.g., a wired network, a wireless network,and/or a carrier network).

For example, as described above, in some examples, wearable device 22and/or patient device 24 may be configured to determine that patient 12is undertaking an activity. However, in some examples, patient device 24may output information indicative of the movement characteristics ofmovement of the arm of patient 12 to cloud 26 with contextualinformation like location or time of day. One or more processors 28 ofcloud 26 may then determine the activity patient 12 is undertaking.Insulin pump 14 may then deliver insulin based on the determinedactivity of patient 12.

One example way in which one or more processors 28 may be configured todetermine that patient 12 is undertaking an activity and determinetherapy to deliver is described in U.S. Patent Publication No.2020/0135320 A1, the entirety of which is incorporated by referenceherein.

The above describes arm movement as a factor in determining whetherpatient 12 is engaging in an activity. However, there may be variousother factors that can be used separately or in combination with armmovement to determine whether patient 12 is engaging in an activity.Time of day and location are two examples of contextual information thatcan be used to determine whether patient 12 is engaging in the activity.For instance, patient 12 may engage in the activity at regular timeintervals and/or at certain locations. Such other factors may becommunicated to one or more processors 28 in any of a variety of ways.For example, patient device 24 may output information about the time ofday and the location of patient 12. In some embodiments, patient device24 may be equipped with a positioning device, such as a globalpositioning system (GPS) unit, and patient device 24 may output locationinformation determined by the GPS unit. In some embodiments, locationinformation may be determined based on known locations of wirelessaccess points and/or cell towers.

There may be various other ways in which one or more processors 28 maydetermine the activity patient 12 is undertaking. This disclosureprovides some example techniques for determining the activity patient 12is undertaking, but the example techniques should not be consideredlimiting.

In some examples, one or more processors 28 may be configured todetermine an amount of insulin (e.g., a bolus dosage of insulin) todeliver to patient 12. As one example, memory accessible by one or moreprocessors 28 may store patient-specific parameters of patient 12 (e.g.,an insulin sensitivity factor, an insulin-to-carbohydrate ratio, etc.).One or more processors 28 may access the memory and, based on the typeof food patient 12 is eating and the patient-specific parameters, maydetermine the amount of insulin that patient 12 is to receive.

In some examples, the amount of insulin may be determined based on apatient-specific physiological simulator referred to herein as a“digital twin.” One or more processors 28 may be configured to utilize a“digital twin” of patient 12 to determine, in a manner that accounts forvarious idiosyncrasies of patient 12, an amount of insulin to bedelivered to patient 12. A digital twin may be a digital replica ofpatient 12 that is based on a mathematical model having one or morepatient-specific parameters (e.g., insulin sensitivity factor, bodyweight, endogenous glucose production, speed of carbohydrate absorption,and/or speed of insulin absorption). The digital twin may be implementedvia software executing on one or more processors 28. The digital twinmay take, as input, information about what patient 12 ate and use thisinformation to simulate postprandial glucose levels and/or to generate arecommendation of how much insulin to deliver to patient 12 to controlthe increase in glucose level resulting from meal consumption.

For example, information about the macronutrient content (e.g.,carbohydrates, protein, and/or fat content) of a meal may be provided toa digital twin of patient 12. Based on the macronutrient content, thedigital twin may be used to predict a time series of glucose amounts tobe absorbed into the bloodstream of patient 12. This may involveconverting an estimated amount of protein into a corresponding amount ofcarbohydrates. Additionally or alternatively, this may involvedetermining the impact of an estimated amount of fat on the rate ofglucose absorption by the digestive system into the bloodstream.

The predicted time series of glucose amounts may be used for a varietyof different purposes (e.g., generating an insulin dosagerecommendation). In some embodiments, the predicted time series ofto-be-absorbed glucose amounts may be used in conjunction with an amountof insulin (e.g., a basal dosage and/or a bolus dosage) to predict atime series of glucose levels (e.g., blood glucose levels orinterstitial glucose levels) resulting from consumption of the meal anddelivery of the amount of insulin. In some embodiments, the predictedtime series of resulting glucose levels may be used to determine anamount of insulin that is “optimal” in that the amount of insulinmaximizes time-in-range. For instance, the digital twin may predict arespective time series of resulting glucose levels for each candidatedosage of a plurality of candidate dosages, and each time series ofresulting glucose levels may be evaluated to determine how many of theresulting glucose levels fall within a target range (e.g., apredetermined target range having an upper limit that is a predeterminednumber of values below a hyperglycemic glucose level and a lower limitthat is a predetermined number of values above a hypoglycemic glucoselevel). The candidate dosage that is selected for recommendation maycorrespond to the time series of resulting glucose levels exhibiting amaximum amount of time (e.g., relative to other time series of resultingglucose levels) within the target range.

One or more processors 28 may communicate the recommendation to patientdevice 24. Responsive to obtaining the recommendation, patient device 24may cause insulin pump 14 to deliver the recommended amount of insulin.For example, patient device 24 may communicate to insulin pump 14 theamount of insulin to deliver.

As described above, a prediction model, such as the digital twin, mayutilize information indicative of the carbohydrates, protein, and/or fatcontent of a meal to determine an amount of insulin sufficient tocounteract a change in glucose level due to consumption of the meal.Using protein and/or fat, in addition to carbohydrates, to determine aninsulin dosage may result in a more accurate dosage determination ascompared to examples where only carbohydrates are utilized. This isbecause protein and fat can affect the amount and/or timing of a mealbolus that is sufficient to counteract a glucose level increase causedby meal consumption.

FIG. 2 is a block diagram illustrating an example glucose levelmanagement system comprising a manual injection device (not shown), inaccordance with one or more examples described in this disclosure. FIG.2 illustrates system 10B that is similar to system 10A of FIG. 1.However, in system 10B, patient 12 may not have insulin pump 14. Rather,patient 12 may utilize a manual injection device (e.g., an insulin penor a syringe) to deliver insulin. For example, rather than insulin pump14 automatically delivering insulin, patient 12 (or a caretaker ofpatient 12) may fill a syringe with insulin, set the dosage amount in aninsulin pen, and/or perform an injection. One or more processors 28 mayuse the techniques of this disclosure to generate an output indicativeof an amount of insulin to deliver to patient 12. One or more processors28 may communicate the output to patient device 24 to present the outputvia a user interface of patient device 24. Patient 12 (or a caretaker)may use the output to provide therapy accordingly.

FIG. 3 is a block diagram illustrating an example glucose levelmanagement system comprising a networked injection device, in accordancewith one or more examples described in this disclosure. FIG. 3illustrates system 10C that is similar to system 10A of FIG. 1 andsystem 10B of FIG. 2. In system 10C, patient 12 may not have insulinpump 14. Rather, patient 12 may utilize injection device 30 to deliverinsulin. For example, rather than insulin pump 14 automaticallydelivering insulin, patient 12 (or a caretaker of patient 12) mayutilize injection device 30 to perform an injection.

Injection device 30 may be different than a syringe because injectiondevice 30 may be a device that can communicate with patient device 24and/or other devices in system 10C. Also, injection device 30 mayinclude a reservoir and, based on information indicative of how muchtherapy dosage to deliver, may be able to dose out that much insulin fordelivery. For example, injection device 30 may automatically set theamount of insulin based on the information received from patient device24. In some examples, injection device 30 may be similar to insulin pump14 but not worn by patient 12. One example of injection device 30 is aninsulin pen, sometimes also called a smart insulin pen. Another exampleof injection device 30 may be an insulin pen with a smart cap, where thesmart cap can be used to set particular doses of insulin. One or moreprocessors 28 may use the techniques of this disclosure to generate anoutput indicative of an amount of insulin to deliver to patient 12. Oneor more processors 28 may be configured to communicate the output topatient device 24 and/or injection device 30 to automatically set thedetermined amount of insulin to be delivered to patient 12.

The above examples describe insulin pump 14, a syringe, and injectiondevice 30 as example ways in which to deliver insulin. In thisdisclosure, the term “insulin delivery device” may generally refer toany device used to deliver insulin. Examples of insulin delivery deviceinclude insulin pump 14, a syringe, and injection device 30. Asdescribed, the syringe may be a device used to inject insulin but is notnecessarily capable of communicating or dosing a particular amount ofinsulin. Injection device 30, however, may be a device used to injectinsulin that may be capable of communicating with other devices (e.g.,via Bluetooth, BLE, and/or Wi-Fi) or may be capable of dosing aparticular amount of insulin. Injection device 30 may be a powered(e.g., battery-powered) device, and the syringe may be a device thatrequires no power.

FIG. 4 is a block diagram illustrating an example of a patient device,in accordance with one or more examples described in this disclosure.While patient device 24 may generally be described as a hand-heldcomputing device, in some embodiments, patient device 24 may be anotebook computer or a workstation, for example. In some examples,patient device 24 may be a mobile device, such as a smartphone or atablet computer. Patient device 24 may execute an application thatallows patient device 24 to perform example techniques described in thisdisclosure. For example, processing circuitry 32 of patient device 24may be configured to obtain user input indicative of one or moremacronutrient values for a meal, communicate the one or moremacronutrient values to one or more processors 28, and obtain a dosagerecommendation indicative of an amount of insulin sufficient tocounteract a glucose level increase caused by consumption of the meal.In some examples, patient device 24 may be a specialized controller forcommunicating with insulin pump 14.

As illustrated in FIG. 4, patient device 24 may include processingcircuitry 32, memory 34, user interface 36, telemetry circuitry 38, andpower source 39. Memory 34 may store program instructions that, whenexecuted by processing circuitry 32, cause processing circuitry 32 toprovide the functionality ascribed to patient device 24 throughout thisdisclosure. For example, processing circuitry 32 may be configured toobtain user input indicative of one or more macronutrient values for ameal.

In some examples, memory 34 of patient device 24 may store a pluralityof parameters, such as amounts of insulin to deliver, target glucoselevel, time of delivery, etc. Processing circuitry 32 (e.g., throughtelemetry circuitry 38) may output the parameters stored in memory 34 toinsulin pump 14 or injection device 30 for delivery of insulin topatient 12. In some examples, processing circuitry 32 may execute anotification application, stored in memory 34, that outputsnotifications to patient 12, such as notification to take insulin,amount of insulin, and time to take the insulin, via user interface 36.

Memory 34 may include any volatile, non-volatile, fixed, removable,magnetic, optical, or electrical media, such as RAM, ROM, hard disk,removable magnetic disk, memory cards or sticks, NVRAM, EEPROM, flashmemory, and the like. Processing circuitry 32 can take the form one ormore microprocessors, DSPs, ASICs, FPGAs, programmable logic circuitry,or the like, and the functions attributed to processing circuitry 32herein may be embodied as hardware, firmware, software or anycombination thereof.

User interface 36 may include a button or keypad, lights, a microphonefor voice commands, and/or a display, such as a liquid crystal display(LCD). In some examples the display may be a touchscreen. As discussedin this disclosure, processing circuitry 32 may present and receiveinformation relating to therapy via user interface 36. For example,processing circuitry 32 may receive patient input via user interface 36.The patient input may be entered, for example, by pressing a button on akeypad, entering text, or selecting an icon from a touchscreen. Thepatient input may be information indicative of the macronutrient contentof a meal, whether patient 12 took the insulin (e.g., through thesyringe or injection device 30), and other such information.

Telemetry circuitry 38 includes any suitable hardware, firmware,software, or any combination thereof for communicating with anotherdevice, such as a device in cloud 26, insulin pump 14 or injectiondevice 30, as applicable, wearable device 22, and monitoring device 20.Telemetry circuitry 38 may receive communication with the aid of anantenna, which may be internal and/or external to patient device 24.Telemetry circuitry 38 may be configured to communicate with anothercomputing device via wireless communication techniques or directcommunication through a wired connection. Examples of local wirelesscommunication techniques that may be employed to facilitatecommunication between patient device 24 and another computing deviceinclude RF communication according to IEEE 802.11, Bluetooth, or BLEspecification sets, infrared communication, e.g., according to an IrDAstandard, or other standard or proprietary telemetry protocols.Telemetry circuitry 38 may also provide connection with carrier networkfor access to cloud 26. In this manner, other devices may be capable ofcommunicating with patient device 24.

Power source 39 delivers operating power to the components of patientdevice 24. In some examples, power source 39 may include a battery, suchas a rechargeable or non-rechargeable battery. A non-rechargeablebattery may last for several months or years, while a rechargeablebattery may be periodically charged from an external device, e.g., on adaily or weekly basis. Recharging of a rechargeable battery may beaccomplished by using an alternating current (AC) outlet or throughproximal inductive interaction between an external charger and aninductive charging coil within patient device 24.

FIG. 5A is an example glucose profile 500A comprising a plot of glucoselevel over time after consuming a meal. The plot shown in FIG. 5Adepicts the time at which the meal is consumed as time zero. In someexamples, one or more processors 28 may simulate glucose level over timeunder the assumption that the meal is consumed instantaneously. In otherwords, one or more processors 28 may model the meal as a set ofmacronutrient values consumed by patient 12 at a single point in time.Alternatively, one or more processors 28 may be configured to model themeal as a set of macronutrient values consumed by patient 12 over anon-instantaneous period of time. For example, one or more processors 28can model the meal as a set of two or more impulses over a period oftime, as one or more rectangular waveforms, as one or more triangularwaveforms, as one or more trapezoidal waveforms, and/or any combinationthereof.

When patient 12 consumes the meal, patient 12's glucose level increasesto a peak of the glucose level increase at a time labeled “timing of thepeak of the glucose level increase.” After reaching the peak of theglucose level increase (e.g., the maximum glucose level), patient 12'sglucose level decreases. Glucose profile 500A may represent bloodglucose levels as a function of time. Glucose profile 500A may beaffected by the net movement of glucose molecules into and out of thebloodstream. Glucose molecules entering the bloodstream from thedigestive system may contribute to a glucose level increase, and glucosemolecules exiting the bloodstream due to the effects of insulin maycontribute to a glucose level decrease. Preferably, the insulin isdelivered to patient 12 in an amount that is sufficient to keep the peakof the glucose level increase below an upper limit of a target range aswell as to keep subsequent glucose levels above a lower limit of thetarget range.

Among the factors affecting glucose profile 500A is the glucoseabsorption rate of the digestive system. As the consumed meal passesthrough the digestive system of patient 12, the digestive system absorbssome of the molecules of the meal. In some examples, the rate ofabsorption of molecules (e.g., glucose molecules) may be based on thequantity of molecules that are passing through the digestive system(e.g., the stomach and intestines). In general, the larger the number ofglucose molecules in the digestive system, the higher the rate ofabsorption of glucose molecules from the digestive system into thebloodstream. The number of glucose molecules in the digestive system maybe determined by the net movement of glucose molecules into and out ofthe digestive system. As the digestive system absorbs glucose moleculesinto the bloodstream of patient 12, the number of glucose molecules inthe digestive system may decrease. However, as glucose molecules areintroduced into the digestive system by the meal, the number of glucosemolecules in the digestive system may increase.

FIG. 5B is an example plot of glucose profiles 500B, 510B, and 520B overtime after consuming a meal. As mentioned above, a meal can include anumber of macronutrients (e.g., carbohydrates, protein, and/or fat),each of which can affect the glucose absorption rate of the digestivesystem. Thus, using a simplistic prediction model that accounts for onlythe carbohydrate content of a meal may result in glucose levelmismanagement. For example, ten grams of protein may have the sameeffect on glucose levels as one gram of carbohydrates. Thus, failing toaccount for the protein content of a meal may result in underestimatingthe glucose level increase caused by the meal and/or underestimating theamount of insulin for counteracting the glucose level increase. Glucoseprofile 500B represents the glucose level over time after consuming ameal including only carbohydrates (i.e., a control group). Glucoseprofile 510B represents the glucose level over time after consuming ameal including carbohydrates and protein. The peak of glucose profile510B is higher than the peak of glucose profile 500B because the proteinin the meal represented by glucose profile 510B is equivalent to anamount of carbohydrates.

As another example, fat can affect the timing and magnitude of the peakof the glucose level increase, because fat may cause a decrease in theglucose absorption rate of the digestive system. More specifically, thefat content of a meal may delay the timing of the peak of the glucoselevel increase and reduce the magnitude of the peak of the glucose levelincrease. Moreover, the fat content of a meal may cause glucose levelsto remain elevated at or near the peak of the glucose level increase fora prolonged period of time. Thus, prematurely determining the peak ofthe glucose level increase and assuming that the peak of the glucoselevel increase is short-lived can result in determining an insulindosage that is insufficient to counteract the glucose level increasedcaused by the meal. Glucose profile 520B represents the glucose levelover time after consuming a meal including carbohydrates, fat, andprotein. The peak of glucose profile 520B is lower and delayed ascompared to the peak of glucose profile 500B because the mealrepresented by glucose profile 520B has a fat content that slows theabsorption of carbohydrates. For a meal with a relatively low fatcontent, the peak of the glucose level increase may have a magnitudethat is closer to the peak of the glucose level caused by acarbohydrate-only meal.

FIGS. 6-8 are flow diagrams illustrating example techniques for glucoselevel management, in accordance with the techniques of this disclosure.The example techniques can be performed by one or more processors suchas one or more processors 28, processing circuitry 32, and/or one ormore processors of insulin pump 14.

One or more processors 28 may be configured to obtain one or moremacronutrient values for a meal (600). For example, the one or moremacronutrient values may be obtained from patient device 24. In someembodiments, the one or more macronutrient values may include anestimated carbohydrate value for the meal. Additionally oralternatively, the one or more macronutrient values may include anestimated protein value and/or an estimated fat value for the meal. Oneor more processors 28 can use the one or more macronutrient values todetermine the rate at which the digestive system of patient 12 absorbsglucose molecules.

One or more processors 28 may be configured to determine an amount ofinsulin to deliver to patient 12 to counteract a glucose level increasecaused by consumption of the meal (602). The amount of insulin maycorrespond to a meal bolus.

In some embodiments, the amount of insulin may be determined based onusing a digital twin of patient 12 to predict amounts of glucose to beabsorbed into the bloodstream of patient 12 over a duration of time dueto consumption of the meal. For example, as mentioned above, one or moreprocessors 28 may determine a glucose absorption rate of the digestivesystem based on the one or more macronutrient values. Based on theglucose absorption rate of the digestive system, one or more processors28 may predict amounts of glucose to be absorbed into the bloodstream ofpatient 12 over a duration of time due to consumption of the meal. Morespecifically, the predicted amounts of glucose to be absorbed into thebloodstream over time may be derived from the glucose absorption rate ofthe digestive system based on the speed of carbohydrate absorption forpatient 12. As will be described in greater detail below, the predictedamounts of glucose to be absorbed into the bloodstream may be used todetermine a bolus dosage of insulin to deliver to patient 12.

In some embodiments, the bolus dosage may be determined based on usingthe digital twin of patient 12 to predict postprandial glucose levels.As mentioned above, the digital twin may be configured to predictpostprandial glucose levels based on the net movement of glucose intoand out of the bloodstream. Glucose entering the bloodstream includesthe predicted amounts of glucose to be absorbed into the bloodstream,and glucose exiting the bloodstream includes glucose absorbed intointerstitial fluid due to the effects of insulin.

In some embodiments, the digital twin may predict postprandial glucoselevels to determine an amount of the bolus dosage that is “optimal” inthat it maximizes time-in-range. For example, one or more processors 28may identify a plurality of candidate dosages. Based on the predictedamounts of glucose to be absorbed into the bloodstream over time and theamount of each candidate dosage, the digital twin of patient 12 may beused to predict a respective time series of glucose values for eachcandidate dosage. Each time series of glucose values may be evaluated todetermine a respective amount of time within a target range for patient12's glucose levels, and one or more processors 28 may select thecandidate dosage that corresponds to a maximum amount of time within thetarget range.

In some embodiments, the digital twin may predict postprandial glucoselevels to determine an amount of the bolus dosage to supplement apreviously delivered amount of insulin. For example, one or moreprocessors 28 may obtain (e.g., from patient device 24) an amount ofinsulin (e.g., a basal dosage) previously delivered to patient 12. Basedon the amount of insulin previously delivered and the predicted amountsof glucose to be absorbed into the bloodstream, one or more processors28 may simulate postprandial glucose levels of patient 12. Thus, one ormore processors 28 may determine the bolus dosage to be an additionalamount of insulin for maximizing time-in-range.

One or more processors 28 may be configured to generate an outputindicative of the amount of insulin to deliver to patient 12 (604). Theoutput may also include a time at which the insulin can be delivered topatient 12. By accounting for the estimated fat and/or protein contentof a meal, one or more processors 28 may be able to generate an outputthat is more accurately tailored to patient 12 and to the meal to beconsumed.

In some examples, one or more processors 28 are configured to cause aninsulin device to deliver the determined amount of insulin as a bolus.The amount of insulin may be a single dose or multiple doses over time.Additionally or alternatively, one or more processors 28 may beconfigured to store the output to memory and/or transmit the output toanother device. The determined amount of insulin can be automaticallydelivered to patient 12 and/or presented to patient 12 via a display.

In the example shown in FIG. 7, one or more processors 28 may beconfigured to obtain an estimated protein value for a meal (700). Asmentioned above, one or more processors 28 may obtain the estimatedprotein value from patient device 24 in some examples.

One or more processors 28 may be further configured to convert theestimated protein value to a first equivalent carbohydrate value (702).One or more processors 28 can convert the estimated protein value to thefirst equivalent carbohydrate value by multiplying or dividing theestimated protein value by a conversion factor or by applying anothermathematical function, such as an exponential or a logarithmic function,involving the conversion factor. The conversion factor may convert theestimated protein value to a parameter approximating the effect ofprotein on glucose levels of patient 12. For example, the digital twinmay approximate the effect of protein as exhibiting a smaller (relativeto carbohydrates) but linear contribution to glucose levels. Thus, theconversion factor may scale down the estimated protein value to acorresponding carbohydrate value that can be added to the estimatedcarbohydrate content of the meal.

Although FIG. 7 is discussed in terms of protein, a similar approach maybe used for other macronutrients. For example, the fat content of a mealmay also impact the total equivalent carbohydrates of the meal.Accordingly, one or more processors 28 may be configured to determinethe total equivalent carbohydrate value for a meal based on theestimated fat value for the meal. However, each gram of fat in a mealmay have a smaller effect on the total equivalent carbohydrate value forthe meal than each gram of protein in the meal. Thus, one or moreprocessors 28 may use a different (e.g., larger) conversion factor forfat than the conversion factor for protein.

The conversion factor may vary from patient to patient and/or from mealto meal. In some examples, one or more processors 28 may be configuredto determine a patient-specific conversion factor based on historicalmeal data for patient 12 and further based on historical glucose leveldata for patient 12. In some examples, one or more processors 28 can useoptimization procedures and/or machine learning to determine a best-fitconversion factor for each meal. In some examples, one or moreprocessors 28 may be configured to determine a patient-specificconversion factor by determining the mean or median of the best-fitconversion factors. In some examples, one or more processors 28 may beconfigured to determine the conversion factor for a meal based on a timeof day that patient 12 consumes the meal.

One or more processors 28 may be configured to determine a totalequivalent carbohydrate value for the meal based on the first equivalentcarbohydrate value(704). Thus, for a meal including thirty grams ofcarbohydrates and ten grams of protein, one or more processors 28 mayfirst convert the protein value to a first equivalent carbohydrate valueof, for example, two grams using a conversion factor value of five.Alternatively, for a conversion factor value of ten, one or moreprocessors 28 can determine a first equivalent carbohydrate value of onegram. One or more processors 28 may then add the first equivalentcarbohydrate value to the estimated carbohydrate value to determine atotal equivalent carbohydrate value for the meal of thirty-one orthirty-two grams. For two meals that are otherwise identical, one ormore processors 28 may be configured to determine a higher totalequivalent carbohydrate value for a meal with a higher protein valuethan a meal with a lower protein value.

One or more processors 28 may be configured to determine an amount ofinsulin based on the total equivalent carbohydrate value (706). In someexamples, based on the total equivalent carbohydrate value for the meal,the digital twin of patient 12 may be used to predict amounts of glucoseto be absorbed into the bloodstream of patient 12 over a duration oftime due to consumption of the meal.

In the example shown in FIG. 8, one or more processors 28 may beconfigured to obtain an estimated fat value for a meal (800). Asmentioned above, one or more processors 28 may obtain the estimated fatvalue from patient device 24 in some examples.

One or more processors 28 may be further configured to determine anabsorption rate tuning factor for patient 12, where the absorption ratetuning factor approximates an effect of fat on an absorption rate ofglucose (802). In some embodiments, the absorption rate tuning factormay be used to approximate or limit the effect of fat on reducing theglucose absorption rate.

The absorption rate tuning factor may vary from patient to patientand/or from meal to meal. In some examples, one or more processors 28may be configured to determine a patient-specific absorption rate tuningfactor based on historical meal data for patient 12 and further based onhistorical glucose level data for patient 12. In some examples, one ormore processors 28 can use optimization procedures and/or machinelearning to determine a best-fit absorption rate tuning factor for eachmeal. In some examples, one or more processors 28 may be configured todetermine a patient-specific absorption rate tuning factor bydetermining the mean or median of the best-fit absorption rate tuningfactors. In some examples, one or more processors 28 may be configuredto periodically update the absorption rate tuning factor, because apatient-specific absorption rate tuning factor can change over time(e.g., over months or years). In some examples, one or more processors28 may be configured to determine the absorption rate tuning factor fora meal based on a time of day that patient 12 consumes the meal.

One or more processors 28 may be configured to determine a glucoseabsorption rate based on the absorption rate tuning factor and theestimated fat value for the meal (804). The glucose absorption rate maycorrespond to the rate at which glucose is absorbed by the digestivesystem into the bloodstream of patient 12. In some examples, one or moreprocessors 28 are configured to determine that a first meal will have afaster absorption rate than a second meal if the first meal has a lowerfat content, assuming that the first and second meals are otherwiseidentical.

One or more processors 28 may be configured to use Equation (1) shownbelow for determining a glucose absorption rate, which is represented byS, based on the estimated fat value and the absorption rate tuningfactor, which is represented by omega. The glucose absorption rate inEquation (1) may become saturated towards an upper limit as the fatvalue in the meal increases. Equation (1) also includes optionalvariables C₁ and C₂. In some examples, one or more processors 28 may beconfigured to use a value of the speed of carbohydrate absorption, or amultiple thereof, as a value for the variable C₁. For very large fatcontents or very small values of omega, the glucose absorption rate mayapproach (e.g., asymptotically approach) C₂. For very low fat contentsor very large values of omega, the glucose absorption rate may approach(e.g., asymptotically approach) C₁+C₂.

$\begin{matrix}{S = {{C_{1} \times \frac{\Omega}{{fat} + \Omega}} + C_{2}}} & (1)\end{matrix}$

Although FIG. 8 is discussed in terms of fat, a similar approach may beused for other macronutrients. For example, one or more processors 28may be configured to determine the glucose absorption rate based on theprotein value of the meal, but the protein value may have a smallereffect on the glucose absorption rate than the fat value.

One or more processors 28 may be configured to determine an amount ofinsulin based on the glucose absorption rate (806). In some examples,based on the glucose absorption rate, one or more processors 28 maydetermine a peak of the glucose level increase caused by consumption ofthe meal. In some examples, based on the glucose absorption rate, one ormore processors 28 may further determine a timing for the peak.

The following numbered examples demonstrate one or more aspects of thedisclosure.

Example 1. A processor-implemented method includes obtaining anestimated protein value for a meal and determining an equivalentcarbohydrate value for the meal based on the estimated protein value.The method also includes determining, based on the equivalentcarbohydrate value for the meal, an amount of insulin to deliver to apatient to counteract a glucose level increase caused by consumption ofthe meal. The method further includes generating an output indicative ofthe amount of insulin to deliver to the patient to counteract theglucose level increase caused by consumption of the meal.

Example 2. The method of example 1, further including obtaining anestimated fat value for a meal, determining, based on the estimated fatvalue, a glucose absorption rate resulting from consumption of the meal,wherein determining the amount of insulin to deliver to the patient isbased on the equivalent carbohydrate value for the meal and is furtherbased on the glucose absorption rate resulting from consumption of themeal.

Example 3. A method includes obtaining an estimated fat value for a mealand determining, based on the estimated fat value, a glucose absorptionrate resulting from consumption of the meal. The method also includesdetermining, based on the glucose absorption rate resulting fromconsumption of the meal, an amount of insulin to deliver to a patient tocounteract a glucose level increase caused by consumption of the meal.The method further includes generating an output indicative of theamount of insulin to deliver to the patient to counteract the glucoselevel increase caused by consumption of the meal.

Example 4. The method of example 3, further including obtaining anestimated protein value for a meal, determining an equivalentcarbohydrate value for the meal based on the estimated protein valuemeal, wherein determining the amount of insulin to deliver to thepatient is based on the glucose absorption rate resulting fromconsumption of the meal and is further based on the equivalentcarbohydrate value for the meal.

Example 5. The method of the preceding examples or any combinationthereof, wherein determining the amount of insulin to deliver to thepatient includes predicting, based on the equivalent carbohydrate valuefor the meal, amounts of glucose to be absorbed into a bloodstream ofthe patient over a duration of time due to consumption of the meal.

Example 6. The method of the preceding examples or any combinationthereof, wherein determining the amount of insulin to deliver to thepatient includes determining, based on the predicted amounts of glucoseto be absorbed into the bloodstream, a bolus dosage of insulin todeliver to the patient.

Example 7. The method of example 4, wherein determining the amount ofinsulin to deliver to the patient includes prior to determining thebolus dosage, obtaining an amount of a basal dosage of insulinpreviously delivered to the patient, and wherein determining the bolusdosage is further based on the amount of the basal dosage of insulin.

Example 8. The method of the preceding examples or any combinationthereof, wherein determining a bolus dosage includes identifying aplurality of candidate dosages and, for each candidate dosage of theplurality of candidate dosages, predicting a respective amount of timewithin a target range for the patient's glucose levels,

Example 9. The method of the preceding examples or any combinationthereof, wherein determining a bolus dosage includes selecting acandidate dosage that results in a maximum amount of time within thetarget range.

Example 10. The method of the preceding examples or any combinationthereof, wherein determining the equivalent carbohydrate value includesdetermining a conversion factor for converting protein to a parameterapproximating an effect of the protein on glucose levels of the patient.

Example 11. The method of the preceding examples or any combinationthereof, wherein determining the equivalent carbohydrate value includesdetermining the equivalent carbohydrate for the meal based on theestimated protein value for the meal and the conversion factor.

Example 12. The method of the preceding examples or any combinationthereof, wherein the conversion factor is determined based on historicalmeal data for the patient and further based on historical glucose leveldata for the patient.

Example 13. The method of the preceding examples or any combinationthereof, wherein the conversion factor is determined based on a time ofday that the patient consumes the meal.

Example 14. The method of the preceding examples or any combinationthereof, wherein determining the glucose absorption rate includesdetermining an absorption rate tuning factor for approximating an effectof fat on the glucose absorption rate.

Example 15. The method of the preceding examples or any combinationthereof, wherein determining the glucose absorption rate includesdetermining the glucose absorption rate based on the estimated fat valuefor the meal and the absorption rate tuning factor.

Example 16. The method of the preceding examples or any combinationthereof, wherein an absorption rate tuning factor is determined based onhistorical meal data for the patient and further based on historicalglucose level data for the patient.

Example 17. The method of the preceding examples or any combinationthereof, wherein determining the amount of insulin to deliver to thepatient includes determining, based on the glucose absorption rate, apeak of the glucose level increase caused by consumption of the meal.

Example 18. The method of the preceding examples or any combinationthereof, wherein determining the amount of insulin to deliver to thepatient includes determining, based on the peak, the amount of insulinto deliver to the patient.

Example 19. The method of the preceding examples or any combinationthereof, wherein determining the amount of insulin to deliver to thepatient includes determining, based on the glucose absorption rate, atiming for a peak of the glucose level increase cause by consumption ofthe meal.

Example 20. The method of the preceding examples or any combinationthereof, wherein determining the amount of insulin to deliver to thepatient includes determining, based on the timing for the peak, theamount of insulin to deliver to the patient.

Example 21. A system includes one or more processors and one or moreprocessor-readable storage media storing instructions which, whenexecuted by the one or more processors, cause performance of the methodof the preceding examples or any combination thereof.

Example 22. A device includes a computer-readable medium havingexecutable instructions stored thereon, configured to be executable byprocessing circuitry for causing the processing circuitry to perform themethod of the preceding examples or any combination thereof.

Example 23. A system comprising means for performing the method of thepreceding examples or any combination thereof.

Various aspects of the techniques may be implemented within one or moreprocessors, including one or more microprocessors, DSPs, ASICs, FPGAs,or any other equivalent integrated or discrete logic circuitry, as wellas any combinations of such components, embodied in programmers, such asphysician or patient programmers, electrical stimulators, or otherdevices. The term “processor” or “processing circuitry” may generallyrefer to any of the foregoing logic circuitry, alone or in combinationwith other logic circuitry, or any other equivalent circuitry.

In one or more examples, the functions described in this disclosure maybe implemented in hardware, software, firmware, or any combinationthereof. If implemented in software, the functions may be stored on, asone or more instructions or code, a computer-readable medium andexecuted by a hardware-based processing unit. Computer-readable mediamay include computer-readable storage media forming a tangible,non-transitory medium. Instructions may be executed by one or moreprocessors, such as one or more DSPs, ASICs, FPGAs, general purposemicroprocessors, or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor,” as used herein may referto one or more of any of the foregoing structure or any other structuresuitable for implementation of the techniques described herein.

In addition, in some aspects, the functionality described herein may beprovided within dedicated hardware and/or software modules. Depiction ofdifferent features as modules or units is intended to highlightdifferent functional aspects and does not necessarily imply that suchmodules or units must be realized by separate hardware or softwarecomponents. Rather, functionality associated with one or more modules orunits may be performed by separate hardware or software components, orintegrated within common or separate hardware or software components.Also, the techniques could be fully implemented in one or more circuitsor logic elements. The techniques of this disclosure may be implementedin a wide variety of devices or apparatuses, including one or moreprocessors 28 of cloud 26, one or more processors of patient device 24,one or more processors of wearable device 22, one or more processors ofinsulin pump 14, or some combination thereof. The one or more processorsmay be one or more integrated circuits (ICs), and/or discrete electricalcircuitry, residing in various locations in the example systemsdescribed in this disclosure.

The one or more processors or processing circuitry utilized for exampletechniques described in this disclosure may be implemented asfixed-function circuits, programmable circuits, or a combinationthereof. Fixed-function circuits refer to circuits that provideparticular functionality, and are preset on the operations that can beperformed. Programmable circuits refer to circuits that can beprogrammed to perform various tasks, and provide flexible functionalityin the operations that can be performed. For instance, programmablecircuits may execute software or firmware that cause the programmablecircuits to operate in the manner defined by instructions of thesoftware or firmware. Fixed-function circuits may execute softwareinstructions (e.g., to receive parameters or output parameters), but thetypes of operations that the fixed-function circuits perform aregenerally immutable. In some examples, the one or more of the units maybe distinct circuit blocks (fixed-function or programmable), and in someexamples, the one or more units may be integrated circuits. Theprocessors or processing circuitry may include arithmetic logic units(ALUs), elementary function units (EFUs), digital circuits, analogcircuits, and/or programmable cores, formed from programmable circuits.In examples where the operations of the processors or processingcircuitry are performed using software executed by the programmablecircuits, memory accessible by the processors or processing circuitrymay store the object code of the software that the processors orprocessing circuitry receive and execute.

Various aspects of the disclosure have been described. These and otheraspects are within the scope of the following claims.

What is claimed is:
 1. A system comprising: one or more processors; andone or more processor-readable storage media storing instructions which,when executed by the one or more processors, cause performance of:obtaining an estimated fat value for a meal; determining, based on theestimated fat value, a glucose absorption rate resulting fromconsumption of the meal; determining, based on the glucose absorptionrate resulting from consumption of the meal, an amount of insulin todeliver to a patient to counteract a glucose level increase caused byconsumption of the meal; and generating an output indicative of theamount of insulin to deliver to the patient to counteract the glucoselevel increase caused by consumption of the meal.
 2. The system of claim1, wherein determining the amount of insulin to deliver to the patientcomprises: predicting, based on the glucose absorption rate resultingfrom consumption of the meal, amounts of glucose to be absorbed into abloodstream of the patient over a duration of time due to consumption ofthe meal; and determining, based on the predicted amounts of glucose tobe absorbed into the bloodstream, a bolus dosage of insulin to deliverto the patient.
 3. The system of claim 2, wherein determining the amountof insulin to deliver to the patient comprises: prior to determining thebolus dosage, obtaining an amount of a basal dosage of insulinpreviously delivered to the patient, and wherein determining the bolusdosage is further based on the amount of the basal dosage of insulin. 4.The system of claim 2, wherein determining the bolus dosage comprises:identifying a plurality of candidate dosages; for each candidate dosageof the plurality of candidate dosages, predicting a respective amount oftime within a target range for the patient's glucose levels; andselecting a candidate dosage that corresponds to a maximum amount oftime within the target range.
 5. The system of claim 1, whereindetermining the glucose absorption rate comprises: determining anabsorption rate tuning factor for approximating an effect of fat on theglucose absorption rate; and determining the glucose absorption ratebased on the estimated fat value for the meal and the absorption ratetuning factor.
 6. The system of claim 5, wherein the absorption ratetuning factor is determined based on historical meal data for thepatient and further based on historical glucose level data for thepatient.
 7. The system of claim 1, wherein determining the amount ofinsulin to deliver to the patient comprises: determining, based on theglucose absorption rate, a peak of the glucose level increase caused byconsumption of the meal; and determining, based on the peak, the amountof insulin to deliver to the patient.
 8. The system of claim 1, whereindetermining the amount of insulin to deliver to the patient comprises:determining, based on the glucose absorption rate, a timing for a peakof the glucose level increase cause by consumption of the meal; anddetermining, based on the timing for the peak, the amount of insulin todeliver to the patient.
 9. A processor-implemented method comprising:obtaining an estimated fat value for a meal; determining, based on theestimated fat value, a glucose absorption rate resulting fromconsumption of the meal; determining, based on the glucose absorptionrate resulting from consumption of the meal, an amount of insulin todeliver to a patient to counteract a glucose level increase caused byconsumption of the meal; and generating an output indicative of theamount of insulin to deliver to the patient to counteract the glucoselevel increase caused by consumption of the meal.
 10. The method ofclaim 9, wherein determining the amount of insulin to deliver to thepatient comprises: predicting, based on the glucose absorption rateresulting from consumption of the meal, amounts of glucose to beabsorbed into a bloodstream of the patient over a duration of time dueto consumption of the meal; and determining, based on the predictedamounts of glucose to be absorbed into the bloodstream, a bolus dosageof insulin to deliver to the patient.
 11. The method of claim 10,wherein determining the amount of insulin to deliver to the patientcomprises: prior to determining the bolus dosage, obtaining an amount ofa basal dosage of insulin previously delivered to the patient, andwherein determining the bolus dosage is further based on the amount ofthe basal dosage of insulin.
 12. The method of claim 10, whereindetermining the bolus dosage comprises: identifying a plurality ofcandidate dosages; for each candidate dosage of the plurality ofcandidate dosages, predicting a respective amount of time within atarget range for the patient's glucose levels; and selecting a candidatedosage that corresponds to a maximum amount of time within the targetrange.
 13. The method of claim 9, wherein determining the glucoseabsorption rate comprises: determining an absorption rate tuning factorfor approximating an effect of fat on the glucose absorption rate; anddetermining the glucose absorption rate based on the estimated fat valuefor the meal and the absorption rate tuning factor.
 14. The method ofclaim 13, wherein the absorption rate tuning factor is determined basedon historical meal data for the patient and further based on historicalglucose level data for the patient.
 15. The method of claim 9, whereindetermining the amount of insulin to deliver to the patient comprises:determining, based on the glucose absorption rate, a peak of the glucoselevel increase caused by consumption of the meal; and determining, basedon the peak, the amount of insulin to deliver to the patient.
 16. Themethod of claim 9, wherein determining the amount of insulin to deliverto the patient comprises: determining, based on the glucose absorptionrate, a timing for a peak of the glucose level increase cause byconsumption of the meal; and determining, based on the timing for thepeak, the amount of insulin to deliver to the patient.
 17. One or morenon-transitory processor-readable storage media storing instructionswhich, when executed by one or more processors, cause performance of:obtaining an estimated fat value for a meal; determining, based on theestimated fat value, a glucose absorption rate resulting fromconsumption of the meal; determining, based on the glucose absorptionrate resulting from consumption of the meal, an amount of insulin todeliver to a patient to counteract a glucose level increase caused byconsumption of the meal; and generating an output indicative of theamount of insulin to deliver to the patient to counteract the glucoselevel increase caused by consumption of the meal.
 18. The one or morenon-transitory processor-readable storage media of claim 17, whereindetermining the glucose absorption rate comprises: determining anabsorption rate tuning factor for approximating an effect of fat on theglucose absorption rate; and determining the glucose absorption ratebased on the estimated fat value for the meal and the absorption ratetuning factor.
 19. The one or more non-transitory processor-readablestorage media of claim 18, wherein the absorption rate tuning factor isdetermined based on historical meal data for the patient and furtherbased on historical glucose level data for the patient.
 20. The one ormore non-transitory processor-readable storage media of claim 17,wherein determining the amount of insulin to deliver to the patientcomprises: determining, based on the glucose absorption rate, a timingfor a peak of the glucose level increase cause by consumption of themeal; and determining, based on the timing for the peak, the amount ofinsulin to deliver to the patient.